APPLICATION POTENTIAL OF RESPONSE SURFACE METHOD ON ELECTRO DISCHARGE MACHINING OF AA6061–CENOSPHERE AMCs PREPARED BY COMPOCASTING METHOD

2021 ◽  
pp. 2150056
Author(s):  
ABHIJIT DEY ◽  
KRISHNA MURARI PANDEY ◽  
RAM NARESH RAI ◽  
AKHTAR KHAN ◽  
ANSHUMAN DAS
Keyword(s):  
Response Surface ◽  
Pulse Current ◽  
Desirability Function ◽  
Processing Condition ◽  
Processing Technique ◽  
Homogeneous Distribution ◽  
Optical Micrograph ◽  
Electro Discharge Machining ◽  
Uniform Dispersion ◽  
Pulse On Time

Cenosphere fly ash particles are incorporated into AA6061 alloys with different concentrations ranging from 0[Formula: see text]wt.% to 10[Formula: see text]wt.% using a modified semi-solid metal processing technique. X-ray diffraction patterns were recorded to analyze the morphology of the aluminum-based metal matrix composites (AMCs). The major diffraction peaks of Al, SiO2, Al2O3 and Fe2O3 are distinctly identified which revealed the presence of cenosphere particles and their integrity within the matrix is preserved. The high-resolution optical micrograph identifies the homogeneous distribution and uniform dispersion of the particles. Machinability of the prepared AMCs was investigated by electro discharge machining (EDM) using response surface methodology (RSM). Face-centered CCD of RSM was considered to design the number of experimental runs required. ANOVA was used to explore the influence of selected process parameters and their interactions on the performance characteristics of the systems by developing a second-order quadratic mathematical model for all the responses. Pulse on-time and pulse current were observed to be the most influencing independent variables of EDM system that affect the selected performance measures during spark erosion process. Finally, desirability function approach was employed to optimize the parameters. The optimal processing condition was identified as follows: pulse current: 6 A, pulse on-time: 1010[Formula: see text][Formula: see text]s, percentage of reinforcement: 2% and flushing pressure: 0.2 MPa. Very small percentages of deviation have been observed while comparing with the experimental results obtained for MRR (8.6%), TWR (10.3%) and SR (2.18%).

scholarly journals Response Surface Approach for Optimization of Sinker Electric Discharge Machine Process Parameters on AISI 4140 Alloy Steel

2012 ◽  
Vol 9 (1) ◽  
pp. 99-104 ◽  
Author(s):  
Manish Vishwakarma, ◽  
V. K. Khare ◽  
Vishal Parashar
Keyword(s):  
Surface Roughness ◽  
Response Surface ◽  
Material Removal ◽  
Removal Rate ◽  
Rectangular Plates ◽  
Electro Discharge Machining ◽  
Aisi 4140 ◽  
Discharge Machine ◽  
Time Gap ◽  
Pulse On Time

The purpose of this paper is to determine the optimal factors of the electro-discharge machining (EDM) process investigate feasibility of design of experiment techniques. The work pieces used were rectangular plates of AISI 4140 grade steel alloy. The study of optimized settings of key machining factors like pulse on time, gap voltage, flushing pressure,input current and duty cycle on the material removal,surface roughness is been carried out using central composite design. The output responses measured were material removal rate (MRR) and surface roughness. Mathematical models are proposed for the above responses using response surface methodology (RSM). The results reveals that MRR is more influenced by peak current, duty factor. Finally, the parameters were optimized for maximum MRR with the desired surface roughness

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Modeling and Optimization of Vibration Amplitude in Turning of Ti-6Al-4V Using Response Surface Methodology

2012 ◽  
Vol 217-219 ◽  
pp. 1567-1570
Author(s):  
A.K.M. Nurul Amin ◽  
Muammer Din Arif ◽  
Syidatul Akma Sulaiman
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vibration Amplitude ◽  
Desirability Function ◽  
Cutting Speed ◽  
Vibration Sensor ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Work Piece ◽  
Inferior Surface

Chatter is detrimental to turning operations and leads to inferior surface topography, reduced productivity, dimensional accuracy, and shortened tool life. Avoidance of chatter has mostly been through reliance on heuristics such as: limiting material removal rates or selecting low spindle speeds and shallow depth of cuts. But, modern industries demand increased output and not steady operational limits. Various research efforts have therefore focused on developing mathematical models for chatter formation. However, as yet there is no existent model that meets all experimental verification. This research employed a novel technique based on the synergy of statistical modeling and experimental investigations in order to develop an effective empirical mathematical model for chatter amplitude and to subsequently find optimal machining conditions. Ti-6Al-4V, Titanium alloy, was used as the work-piece due to its increased popularity in applications related to aerospace, automotive, nuclear, medical, marine etc. A sequence of 15 experimental runs was conducted based on a small Central Composite Design (CCD) model in Response Surface Methodology (RSM). The primary (independent) parameters were: cutting speed, feed, and depth of cut. The tool overhang was kept constant at 70 mm. An engine lathe (Harrison M390) was employed for turning purposes. The data acquisition system comprised a vibration sensor (accelerometer) and a signal conditioning unit. The resultant vibrations were analyzed using the DASYLab 5.6 software. The best model was found to be quadratic which had a confidence level of 95% (ANOVA) and insignificant Lack of Fit (LOF) in Fit and Summary analyses. Desirability Function (DF) approach predicted minimum vibration amplitude of 0.0276 Volts and overlay plots identified two preferred machining regimes for optimal vibration amplitude.

Studies of kerf width and surface roughness using the response surface methodology in AA 4032–TiC composites

2021 ◽  
pp. 095440892110414
Author(s):  
TS Senthilkumar ◽  
R Muralikannan ◽  
T Ramkumar ◽  
S Senthil Kumar
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Matrix ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Kerf Width ◽  
Wire Electrical Discharge Machining ◽  
Box Behnken Design ◽  
Pulse On Time

A substantially developed machining process, namely wire electrical discharge machining (WEDM), is used to machine complex shapes with high accuracy. This existent work investigates the optimization of the process parameters of wire electrical discharge machining, such as pulse on time ( Ton), peak current ( I), and gap voltage ( V), to analyze the output performance, such as kerf width and surface roughness, of AA 4032–TiC metal matrix composite using response surface methodology. The metal matrix composite was developed by handling the stir casting system. Response surface methodology is implemented through the Box–Behnken design to reduce experiments and design a mathematical model for the responses. The Box–Behnken design was conducted at a confident level of 99.5%, and a mathematical model was established for the responses, especially kerf width and surface roughness. Analysis of variance table was demarcated to check the cogency of the established model and determine the significant process. Surface roughness attains a maximum value at a high peak current value because high thermal energy was released, leading to poor surface finish. A validation test was directed between the predicted value and the actual value; however, the deviation is insignificant. Moreover, a confirmation test was handled for predicted and experimental values, and a minimal error was 2.3% and 2.12% for kerf width and surface roughness, respectively. Furthermore, the size of the crater, globules, microvoids, and microcracks were increased by amplifying the pulse on time.

Optimization and Prediction of Machining Responses Using Response Surface Methodology and Adaptive Neural Network by Wire Electric Discharge Machining of Alloy-X

2021 ◽  
Vol 1026 ◽  
pp. 28-38
Author(s):  
I. Vishal Manoj ◽  
S. Narendranath ◽  
Alokesh Pramanik
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Electric Discharge ◽  
Cutting Speed ◽  
Electric Discharge Machining ◽  
Surface Method ◽  
Wire Electric Discharge Machining ◽  
Pulse On Time

Wire electric discharge machining non-contact machining process based on spark erosion technique. It can machine difficult-to-cut materials with excellent precision. In this paper Alloy-X, a nickel-based superalloy was machined at different machining parameters. Input parameters like pulse on time, pulse off time, servo voltage and wire feed were employed for the machining. Response parameters like cutting speed and surface roughness were analyzed from the L25 orthogonal experiments. It was noted that the pulse on time and servo voltage were the most influential parameters. Both cutting speed and surface roughness increased on increase in pulse on time and decrease in servo voltage. Grey relation analysis was performed to get the optimal parametric setting. Response surface method and artificial neural network predictors were used in the prediction of cutting speed and surface roughness. It was found that among the two predictors artificial neural network was accurate than response surface method.

scholarly journals PLANEJAMENTO DE MISTURAS E VISUALIZAÇÃO DA REGIÃO ÓTIMA COM PLANILHAS NO EXCEL: UM TUTORIAL

Química Nova ◽  
2021 ◽  
Author(s):  
Felipe Hilário ◽  
Jeyne Castro ◽  
Tomas Barros ◽  
Edenir Pereira-Filho
Keyword(s):  
Response Surface ◽  
Desirability Function ◽  
Mixture Design ◽  
Contour Plot ◽  
Data Sets ◽  
Multiple Responses ◽  
Data Treatment ◽  
Optimum Region

MIXTURE DESIGN AND OPTIMUM REGION VISUALIZATION WITH SPREADSHEETS AT EXCEL: A TUTORIAL. This tutorial shows how to perform the data treatment for mixture design with two and multiple responses. Two data sets from the literature were used to exemplify the calculations. A template in Excel was proposed to prepare contour plot and response surface to visualize the optimum region of the mixture, which usually is made with commercial computational programs. The desirability function was used in example 2 (optimization of 5 responses simultaneously), and a code also available for download was proposed. Besides, 21 videos were prepared to show all the details for the readers and, it is available on YouTube. With this tutorial, it is possible to learn in a practical fashion how to handle the data from mixture design and, the authors hope to contribute with the researchers in this area

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